1. Field of the Invention
This invention relates to a pattern recognition apparatus, and more particularly, to a pattern recognition apparatus for controlling parameters in order to efficiently recognize a pattern when an input device included in the apparatus undergoes various kinds of restrictions which can be changed by the parameters.
2. Description of the Related Art
In an image recognition apparatus provided in a robot moving in a three-dimensional space, since it is unknown in which direction an image signal to be recognized is present, the apparatus is requested to input a signal within a range as wide as possible. At the same time, the apparatus is required to have sufficient spatial resolution to recognize a certain pattern. As an image input device which simultaneously satisfies such requirements, a device, which performs nonuniform sampling such that, referring to the characteristics of a human retina, an image near the center of an optical axis is sensed with high resolution and resolution decreases as an image is separated from the optical axis, has been devised.
In such nonuniform sampling, in order to exactly recognize a pattern sampled with low resolution at a portion surrounding an input image, it is necessary to change the optical axis so that the pattern is again sampled in a high-resolution region near the center of the optical axis. That is, nonuniform sampling becomes an effective input method only with optical-axis control.
A method for controlling an optical axis based on features of an input image input according to nonuniform sampling has been devised as an optical-axis control method for the above-described purpose. For example, the absolute value of the slope of an image intensity, an output value after passing through a two-dimensional filter, and the like are used as the features. An optical-axis control method based on a knowledge base has also been devised. In this knowledge-base method, tasks to be executed by a system, and a set of images or patterns to be recognized are expressed by a Bayes network, and an operation to be subsequently performed is determined based on the probability structure in order to efficiently execute given tasks.
As described above, most methods of conventional optical-axis control are based on image signals. Accordingly, an optical axis is controlled only by geometric properties of an image, such as a portion having a large edge intensity or a portion having a large filter output. In such control methods, an optical axis is moved to a portion which is not required for a task if the absolute value of the slope is large. Furthermore, in the above-described knowledge-base method, since the Bayes network is used, the structure of data which can be expressed is limited.